Tales of river and ice: Indigenous art and water justice in the Arctic and the Amazon
Bibliographic record
Abstract
Abstract Indigenous water knowledge recognizes water as living, and that the relationship between people and water is one of reciprocity. Yet, Indigenous Peoples continue to struggle for water justice due to centuries long and ongoing colonial legacies that have intergenerational effects on self-determination, culture, and wellbeing. Using a narrative review, this paper explores how published research has used art and arts-based approaches to explore dimensions of water injustice, wellbeing and mental health with Indigenous communities living in the Arctic and Amazon regions. Within the three central themes of the review (wellbeing, water justice, and arts-based research approaches), the most discussed emergent themes were: relationship to place, kinship, the lived experience of water, ongoing changes to water, and storytelling and art as instruments of resistance and to make visible what is not visible. The paper discusses those themes from the literature, and possible areas of future research. The findings underscore the importance of including diverse voices, worldviews and knowledges in water governance, and the potential for arts-based approaches to facilitate intercultural and intergenerational efforts to address water injustice and advance Indigenous Peoples’ rights to self-determination.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".